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Mechanical Behavior of Representative Volume Element Specimens of Lithium-Ion Battery Modules without and with Electrolyte under Quasi-Static and Dynamic In-Plane Compressive Loading Conditions

SAE International Journal of Materials and Manufacturing

University of Michigan, USA-Eui Seop Kim, Shin-Jang Sung, Jwo Pan
University of Ulsan, Republic of Korea-Sung-Tae Hong
  • Journal Article
  • 05-12-03-0014
Published 2019-07-02 by SAE International in United States
Small rectangular representative volume element (RVE) specimens of lithium-ion battery modules without and with electrolyte were tested under quasi-static and dynamic in-plane constrained compressive loading conditions. Effects of electrolyte and loading rate on the compressive behavior of RVE specimens were examined. The test results show that the average buckling stress of the specimens with electrolyte is higher than that of the specimens without electrolyte under both quasi-static and dynamic loading conditions. The test results also show that the average buckling stress of the specimens under dynamic loading conditions is higher than that of the specimens under quasi-static loading conditions, without or with the presence of electrolyte in the specimens. The percentage of increase of the average buckling stress of the specimens due to electrolyte under dynamic loading conditions is more than that of the specimens under quasi-static loading conditions. The percentage of increase of the average buckling stress for the specimens with electrolyte from the quasi-static to dynamic loading conditions is more than that for the specimens without electrolyte.
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Open Access

Modeling and Optimal Design of All-Wheel-Drive Hybrid Light Trucks

SAE International Journal of Alternative Powertrains

University of Michigan, USA-Ziheng Pan, Huei Peng
Ford Motor Company, USA-Xiaowu Zhang
  • Journal Article
  • 08-08-01-0003
Published 2019-06-06 by SAE International in United States
Fuel economy and performance are both important in the design of hybrid pickup trucks. All-wheel drive is essential to ensure superior performance compared to two-wheel-drive designs. In this article, as a comprehensive extension work to the article published in ASME Dynamic Systems and Control Conference [1] on all-wheel-drive (AWD) hybrid truck, we investigate the modeling, design, and control problem of AWD hybrid vehicles and develop a methodology to identify optimal designs. This methodology 1) formulates an automated modeling process, 2) searches exhaustively through all possible AWD designs, and 3) employs a near-optimal energy management strategy, to obtain a family of designs with superior performance and fuel economy. A design case study for a hybrid Ford F-150 is conducted, to showcase this design process.
Open Access

Situational Awareness, Driver’s Trust in Automated Driving Systems and Secondary Task Performance

SAE International Journal of Connected and Automated Vehicles

University of Michigan, USA-Luke Petersen, Lionel Robert, Xi Jessie Yang, Dawn Tilbury
  • Journal Article
  • 12-02-02-0009
Published 2019-05-16 by SAE International in United States
Driver assistance systems, also called automated driving systems, allow drivers to immerse themselves in non-driving-related tasks. Unfortunately, drivers may not trust the automated driving system, which prevents either handing over the driving task or fully focusing on the secondary task. We assert that enhancing situational awareness (SA) can increase a driver’s trust in automation. SA should increase a driver’s trust and lead to better secondary task performance. This study manipulated drivers’ SA by providing them with different types of information: the control condition provided no information to the driver, the low condition provided a status update, while the high condition provided a status update and a suggested course of action. Data collected included measures of trust, trusting behavior, and task performance through surveys, eye-tracking, and heart rate data. Results show that SA both promoted and moderated the impact of trust in the automated vehicle (AV), leading to better secondary task performance. This result was evident in measures of self-reported trust and trusting behavior.